Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China

In this study, a copula-based stochastic fuzzy-credibility programming (CSFP) method is developed for planning regional-scale electric power systems (REPS). CSFP cannot only deal with multiple uncertainties presented as random variables, fuzzy sets, interval values as well as their combinations, but also reflect uncertain interactions among multiple random variables owning different probability distributions and having previously unknown correlations. Then, a CSFP-REPS model is formulated for planning the electric power systems (EPS) of the Jing-Jin-Ji region, where multiple scenarios with different joint and individual probabilities as well as different credibility levels are examined. Results reveal that electricity shortage would offset [4.8, 5.2]% and system cost would reduce [3.2, 3.3]% under synergistic effect scheme. Results also disclose that the study region’s future electricity-supply pattern would tend to the transition to renewable energies and the share of renewable energies would increase approximately 10% over the planning horizon. Compared to the conventional stochastic programming, the developed CSFP method can more effectively analyze individual and interactive effects of multiple random variables, so that the loss of uncertain information can be mitigated and the robustness of solution can be enhanced. Moreover, based on the main effect analysis and regression analysis, CSFP-REPS can provide multiple joint planning strategies in a cost- and computation-effective way. Findings are useful for reflecting interactions among multiple random variables and disclosing their joint effects on modeling outputs of REPS planning problems.

[1]  Y. P. Li,et al.  A multistage fuzzy-stochastic programming model for supporting sustainable water-resources allocation and management , 2009, Environ. Model. Softw..

[2]  Charudatta M. Kshirsagar,et al.  Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses , 2017 .

[3]  Ross Baldick,et al.  Multi-year stochastic generation capacity expansion planning under environmental energy policy , 2016 .

[4]  Guohe Huang,et al.  A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin , 2018 .

[5]  Bo Egardt,et al.  Cooperative energy management of automated vehicles , 2016 .

[6]  Joakim Widén,et al.  Large-scale integration of photovoltaic power in a distribution grid using power curtailment and energy storage , 2017 .

[7]  Gordon H. Huang,et al.  A hybrid fuzzy-stochastic technique for planning peak electricity management under multiple uncertainties , 2017, Eng. Appl. Artif. Intell..

[8]  Gordon H. Huang,et al.  Identification of optimal strategies for energy management systems planning under multiple uncertainties , 2009 .

[9]  Jean-Louis Scartezzini,et al.  Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid , 2017 .

[10]  Guohe Huang,et al.  Modeling for planning municipal electric power systems associated with air pollution control – A case study of Beijing , 2013 .

[11]  Baoding Liu,et al.  Chance constrained programming with fuzzy parameters , 1998, Fuzzy Sets Syst..

[12]  Chuntian Cheng,et al.  Optimization of peak loads among multiple provincial power grids under a central dispatching authority , 2014 .

[13]  Guohe Huang,et al.  A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning , 2017 .

[14]  Guohe Huang,et al.  Chance-constrained two-stage fractional optimization for planning regional energy systems in British Columbia, Canada , 2015 .

[15]  Guohe Huang,et al.  A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models , 2015 .

[16]  Yurui Fan,et al.  Inexact Fuzzy Stochastic Chance Constraint Programming for Emergency Evacuation in Qinshan Nuclear Power Plant under Uncertainty , 2017 .

[17]  Gordon H. Huang,et al.  IPWM: AN INTERVAL PARAMETER WATER QUALITY MANAGEMENT MODEL , 1996 .

[18]  Risto Lahdelma,et al.  Fuzzy chance constrained linear programming model for optimizing the scrap charge in steel production , 2008, Eur. J. Oper. Res..

[19]  Y. P. Li,et al.  Fuzzy-stochastic-based violation analysis method for planning water resources management systems with uncertain information , 2009, Inf. Sci..

[20]  Carsten Herbes,et al.  Responding to policy change: New business models for renewable energy cooperatives – Barriers perceived by cooperatives’ members , 2017 .

[21]  M. M. Lotfi,et al.  Possibilistic programming approach for mid-term electric power planning in deregulated markets , 2012 .

[22]  Taha B. M. J. Ouarda,et al.  The Gumbel mixed model for flood frequency analysis , 1999 .

[23]  Jung-Hua Wu,et al.  Two-stage stochastic programming model for the regional-scale electricity planning under demand uncertainty , 2016 .

[24]  Reza Ghorbani,et al.  Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices , 2017 .

[25]  Shuxiao Wang,et al.  The impact of the "Air Pollution Prevention and Control Action Plan" on PM2.5 concentrations in Jing-Jin-Ji region during 2012-2020. , 2017, The Science of the total environment.

[26]  Guohe Huang,et al.  Electric-power systems planning and greenhouse-gas emission management under uncertainty , 2012 .

[27]  Hamidreza Zareipour,et al.  A chance constrained programming approach to integrated planning of distributed power generation and natural gas network , 2017 .

[28]  Xue Song,et al.  China's large-scale power shortages of 2004 and 2011 after the electricity market reforms of 2002: Explanations and differences , 2013 .

[29]  Kincho H. Law,et al.  Cooperative wind turbine control for maximizing wind farm power using sequential convex programming , 2015 .

[30]  Guohe Huang,et al.  An interval-possibilistic basic-flexible programming method for air quality management of municipal energy system through introducing electric vehicles. , 2017, The Science of the total environment.

[31]  R. Nelsen.  An Introduction to Copulas , 1998 .

[32]  Xiaoxia Huang,et al.  Credibility-based chance-constrained integer programming models for capital budgeting with fuzzy parameters , 2006, Inf. Sci..

[33]  Boqiang Lin,et al.  Cost of long distance electricity transmission in China , 2017 .

[34]  M. A. Uqaili,et al.  A Review of Energy and Power Planning and Policies of Pakistan , 2017 .

[35]  Inara K. Scott,et al.  Planning for the Future of the Electric Power Sector through Regional Collaboratives , 2015 .

[36]  Surekha Dudhani,et al.  Renewable energy sources for peak load demand management in India , 2006 .

[37]  K. Ponnambalam,et al.  Risk-averse stochastic programming approach for microgrid planning under uncertainty , 2017 .

[38]  Ger Devlin,et al.  Energy Regulations: A Case for Peat and Wood Fibre in Ireland. , 2014 .

[39]  Mitja Brilly,et al.  Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River , 2015 .

[40]  Lixing Yang,et al.  Fuzzy chance-constrained programming with linear combination of possibility measure and necessity measure , 2008 .

[41]  Jarmo Partanen,et al.  Technical, economic and uncertainty modelling of a wind farm project , 2016 .

[42]  Guohe Huang,et al.  Integer fuzzy credibility constrained programming for power system management , 2012 .

[43]  Abraham Charnes,et al.  Response to Decision Problems Under Risk and Chance Constrained Programming: Dilemmas in the Transition , 1983 .

[44]  F Chen,et al.  A copula-based chance-constrained waste management planning method: An application to the city of Regina, Saskatchewan, Canada , 2016, Journal of the Air & Waste Management Association.

[45]  Bor-Sen Chen,et al.  Natural Resource Management for Nonlinear Stochastic Biotic-Abiotic Ecosystems: Robust Reference Tracking Control Strategy Using Limited Set of Controllers , 2016 .

[46]  Goran Krajačić,et al.  Long-term energy planning of Croatian power system using multi-objective optimization with focus on renewable energy and integration of electric vehicles , 2016 .

[47]  An Zeng,et al.  Regional co-control plan for local air pollutants and CO2 reduction: Method and practice , 2017 .

[48]  B. Zhang,et al.  Energy implications of China's regional development: New insights from multi-regional input-output analysis , 2017 .

[49]  Lin Wang,et al.  Risk-based electric power system planning for climate change mitigation through multi-stage joint-probabilistic left-hand-side chance-constrained fractional programming: A Canadian case study , 2018 .

[50]  Guohe Huang,et al.  Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence , 2017 .

[51]  Edward A. McBean,et al.  An Integrated Risk Analysis Method for Planning Water Resource Systems to Support Sustainable Development of An Arid Region , 2017 .

[52]  Guohe Huang,et al.  A robust flexible-probabilistic programming method for planning municipal energy system with considering peak-electricity price and electric vehicle , 2017 .

[53]  Vladimir Simic,et al.  A multi-stage interval-stochastic programming model for planning end-of-life vehicles allocation , 2016 .

[54]  Vladimir Simic,et al.  Interval-parameter chance-constrained programming model for uncertainty-based decision making in tire retreading industry , 2017 .

[55]  Y. P. Li,et al.  Planning carbon dioxide mitigation of Qingdao's electric power systems under dual uncertainties , 2016 .

[56]  V. Singh,et al.  Bivariate Flood Frequency Analysis Using the Copula Method , 2006 .

[57]  Guohe Huang,et al.  Inexact credibility constrained programming for environmental system management , 2011 .

[58]  Guohe Huang,et al.  A fuzzy-stochastic simulation-optimization model for planning electric power systems with considering peak-electricity demand: A case study of Qingdao, China , 2016 .